1. Presented By: Karuna Puri
AWS Emerging
Technologies

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3. Our Agenda
01
04:15 PM AWS Overview
02
04:25 PM AWS Pillars
03
04:30 PM Case Study
04
04:55 PM Certification Tips & Tricks
05
05:00 PM Conclusion

4. AWS Cloud Provider
Overview

5. Amazon Web Services
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● Global data centers
● 200 services
● Secure and robust
● Pay as you go
● Built for business needs

6. Why is AWS customers choice
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2013 2014 2016 2017 Agility
Scale seamlessly
Accelerate Time to market
Reduce management
complexity
Agility
Increase Innovation
Minimize security
vulnerabilities
Optimize Cost
Complexity
&
Risk

7. AWS Services Categories
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8. Connect to an AWS Services

9. Unmanaged Services

10. Managed Services

11. Serverless Services

12. AWS Architecture Pillars

13. AWS Applications & Use-cases
1. Storage & Backup
2. Big Data
3. Social Networking
4. Websites
5. Gaming

14. Case Study

15. Create Data Lake & Catalog
Problem: Setting up & managing data lake today involves lot of manual, complicated, and time-consuming
tasks. It starts from loading data from diverse sources, monitoring those data flows, setting up partitions,
turning on encryption and managing keys, defining transformation jobs and monitoring their operation,
re-organizing data into a columnar format, configuring access control settings, deduplicating redundant data,
matching linked records, granting access to data sets, and auditing access over time. This process can be
simplified with help of different AWS services with defining data sources and what data access and security
policies we want to apply.
Solution: Create a data lake and catalog on cloud which can have different level of ingest sources (like raw
data, transformed data, final processes data as per business expectation). Layers of data could be one of these:
Bronze — Silver — Gold
Let’s understand different steps involved in this Data Lake & Data Catalog creation process.

16. Continued.. Create Data Lake & Catalog
Steps:
Bronze Data Layer (Raw Data)
1. Grant Permissions to IAM user
2. Configure S3 bucket to register data for lake formation
3. Data Ingestion to S3
4. Create Database
5. Crawl Data by Glue
6. Create Data Catalog with Glue
7. Use Athena to Query Data in Lake
Silver/Gold Data Layer (Transformed-Processed Data)
1. Ingest real-time data using Kinesis Firehose
2. Create Database
3. Crawl Data by Glue
4. Data Catalog with Glue
5. Athena - Query Data in Lake
6. Transform data using Glue

17. AWS Certifications:
Tips - Tricks

18. AWS Certifications

19. AWS Cloud Experience

20. Conclusion
Cloud Experience with AWS:
● In terms of services it has better prospects.
● Provides comprehensive integrated cloud services.
● Follows Pay as you go model.
● Availability is there in more than 245 countries and territories, with approx. 29 launched regions and 93
availability zones.
● More security.
● Implementation & Learning is comparatively tough as compared to other providers.
● Account management could be bit tedious & tricky sometimes as each master account has a set of users
and roles defined.
Hence, it depends more on the organization’s existing architecture and requirements to go with which
cloud provider which takes into account factors - cost optimizations, security, reliability, operational
excellence, maintainability, speed, and performance efficiency.

21. References:
1. https://blog.knoldus.com/enterprise-data-lake-cloud-aws/
2. https://blog.knoldus.com/transform-data-on-cloud-with-aws-glue-managed-etl-platform/
3. https://explore.skillbuilder.aws/learn/external-ecommerce;view=none;redirectURL=
4. https://aws.amazon.com/training/classroom/
5. https://aws.amazon.com/training/events/
6. https://aws.amazon.com/training/digital/

22. Thank You !
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